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Content Provider | ACM Digital Library |
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Author | Roth, Dan Wang, Hongning Zhai, ChengXiang Lu, Yue |
Abstract | With more and more people freely express opinions as well as actively interact with each other in discussion threads, online forums are becoming a gold mine with rich information about people's opinions and social behaviors. In this paper, we study an interesting new problem of automatically discovering opposing opinion networks of users from forum discussions, which are subset of users who are strongly against each other on some topic. Toward this goal, we propose to use signals from both textual content (e.g., who says what) and social interactions (e.g., who talks to whom) which are both abundant in online forums. We also design an optimization formulation to combine all the signals in an unsupervised way. We created a data set by manually annotating forum data on five controversial topics and our experimental results show that the proposed optimization method outperforms several baselines and existing approaches, demonstrating the power of combining both text analysis and social network analysis in analyzing and generating the opposing opinion networks. |
Starting Page | 1642 |
Ending Page | 1646 |
Page Count | 5 |
File Format | |
ISBN | 9781450311564 |
DOI | 10.1145/2396761.2398489 |
Language | English |
Publisher | Association for Computing Machinery (ACM) |
Publisher Date | 2012-10-29 |
Publisher Place | New York |
Access Restriction | Subscribed |
Subject Keyword | Opinion analysis Social network analysis Linear programming Online forums Optimization |
Content Type | Text |
Resource Type | Article |
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